Project Overview

This notebook presents the project team’s exploratory data analysis (EDA) of credit card customers segmented by risk level. By combining stress scores, attention indicators, and customer segments, the team identifies priority groups for targeted marketing and risk management strategies.

Objectives:

  • Quantify the proportion of customers needing attention
  • Break down high-risk customers into meaningful segments
  • Identify conversion opportunities for key at-risk groups
  • Provide data-driven recommendations balancing growth and risk

Table of Contents

  1. Project Setup
    • Library imports
    • Data preparation
  2. Customer Risk Distribution
    • Attention vs. no attention breakdown
  3. Segment Analysis
    • Dormant / Casual Users
    • Heavy Credit Users (Revolvers)
    • Balanced Moderate Spenders
  4. Visualization
    • Tree map representation
  5. Recommendations
Out[2]:
cust_id balance balance_frequency purchases oneoff_purchases installments_purchases cash_advance purchases_frequency oneoff_purchases_frequency purchases_installments_frequency cash_advance_frequency cash_advance_trx purchases_trx credit_limit payments minimum_payments prc_full_payment tenure cluster customer_segment utilization min_pay_ratio flag_cash_advance flag_low_payment flag_high_util flag_min_only flag_irregular_balance stress_score risk_level need_attention attention_status
0 C10001 40.900749 0.818182 95.40 0.00 95.4 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12 6 Dormant / Casual Users 0.040901 0.691320 False True False False False 1 low False no attention needed
1 C10002 3202.467416 0.909091 0.00 0.00 0.0 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12 0 Heavy Credit Users (Revolvers) 0.457495 0.261353 False True False False False 1 low False no attention needed
2 C10003 2495.148862 1.000000 773.17 773.17 0.0 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12 2 Balanced Moderate Spenders 0.332687 1.008388 False True False True False 2 low False no attention needed
3 C10004 1666.670542 0.636364 1499.00 1499.00 0.0 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 312.343947 0.000000 12 6 Dormant / Casual Users 0.222223 0.000000 False True False False False 1 low False no attention needed
4 C10005 817.714335 1.000000 16.00 16.00 0.0 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12 6 Dormant / Casual Users 0.681429 0.360871 False True True False False 2 low False no attention needed
attention_status_count
attention_status
attention needed 2412
no attention needed 6538
No description has been provided for this image

Our team calculated the distribution of customers requiring attention versus those who do not. The results show that approximately 26.9% of customers (2,412 out of 8,950) fall into the “needs attention” category. This proportion is significant and highlights a sizable group that warrants closer monitoring and targeted engagement strategies.

Out[5]:
array(['attention needed'], dtype=object)
Out[6]:
customer_segment attention_flag_count attention_flag_percentages
0 Dormant / Casual Users 1051 44.0
1 Heavy Credit Users (Revolvers) 602 25.0
2 Balanced Moderate Spenders 471 20.0
3 Low Balance, Low Engagement 198 8.0
4 Small High-Payment Anomalies 54 2.0
5 VIP High Spenders 36 1.0

Within the 2,412 customers identified as needing attention, the majority fall into three key segments:

  • Dormant / Casual Users (~44%)

    • Behavior: Low spend and engagement, minimal contribution to revenue.
    • Team Conversion Goal: Increase transaction activity and engagement without increasing credit risk.
    • Opportunities: Re-engagement campaigns, category-specific offers, gamification (spend milestones), partnerships with merchants, digital nudges.
    • Risk Control: Maintain low credit exposure and focus on frequency rather than balance growth.

  • Heavy Credit Users / Revolvers (~25%)

    • Behavior: High balances and cash advance usage, minimal repayments — profitable but high risk.
    • Team Conversion Goal: Retain revenue while encouraging healthier repayment habits.
    • Opportunities: Debt consolidation, flexible repayment tools, credit health programs, incentives to shift spend from cash advances to retail, financial coaching.
    • Risk Control: Monitor repayment improvements and restrict further credit increases until stability is observed.

  • Balanced Moderate Spenders (~20%)

    • Behavior: Moderate, predictable spending with low risk but untapped potential.
    • Team Conversion Goal: Move toward premium or high-value segments (VIP spenders or full-payment customers).
    • Opportunities: Tiered reward upgrades, installment plans, lifestyle campaigns (travel, dining), cross-selling premium products, anniversary promotions.

📌 These findings provide the foundation for prioritizing customer conversion strategies that balance revenue growth with risk management.